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fix: [AI-Generated Earnings] The AI-generated earnings feature modifies earnings directly, without first presenting the proposed adjustments to the user for their review and confirmation. #7743

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fix: [AI-Generated Earnings] The AI-generated earnings feature modifies earnings directly, without first presenting the proposed adjustments to the user for their review and confirmation. #7743

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32af114
fix: [Agent] When adding conversation logs to the knowledge base, the…
shaohuzhang1 Nov 9, 2026
7ff153c
fix: [AI-Generated Earnings] The AI-generated earnings feature modifi…
shaohuzhang1 Nov 9, 2026
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10 changes: 6 additions & 4 deletions apps/application/serializers/application_chat_record.py
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Original file line number Diff line number Diff line change
Expand Up @@ -393,15 +393,17 @@ def post_improve(self, instance: Dict, request=None, scope="WORKSPACE", with_val
paragraph_ids = []
problem_paragraph_mappings = []
for chat_record in chat_record_list:
answer_text = "".join([r.get("content") for r in chat_record.messages if r.get("type") == "TEXT"])
problem_text = chat_record.question.get("content")
paragraph = Paragraph(
id=uuid.uuid7(),
document_id=document_id,
content=chat_record.answer_text,
content=answer_text,
knowledge_id=knowledge_id,
title=chat_record.problem_text,
chunks=text_to_chunk(chat_record.answer_text),
title=problem_text,
chunks=text_to_chunk(answer_text),
)
problem, _ = Problem.objects.get_or_create(content=chat_record.problem_text, knowledge_id=knowledge_id)
problem, _ = Problem.objects.get_or_create(content=problem_text, knowledge_id=knowledge_id)
problem_paragraph_mapping = ProblemParagraphMapping(
id=uuid.uuid7(),
knowledge_id=knowledge_id,

@shaohuzhang1 shaohuzhang1 Nov 9, 2026

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Review of the diff

1. Potential None / empty-string issues

  • chat_record.question.get("content") — if question is None, or the dict has no "content" key, this raises AttributeError/KeyError. Use defensive access: 
    problem_text = (chat_record.question or {}).get("content") or ""
  • "".join([r.get("content") for r in chat_record.messages if r.get("type") == "TEXT"]) — r.get("content") can return None, which will raise TypeError: sequence item N: expected str instance, NoneType found. Filter or coerce: 
    answer_text = "".join(
    r["content"] for r in chat_record.messages
    if r.get("type") == "TEXT" and r.get("content")
)

2. Behavior change worth confirming

  • The previous code used chat_record.answer_text / chat_record.problem_text (presumably denormalized fields or properties on the model). The new code recomputes them from messages / question. Make sure:
    • chat_record.messages is always populated and ordered correctly.
    • Concatenating multiple TEXT messages without a separator ("") is intentional — otherwise use "\n".
    • Any whitespace/None values are trimmed before chunking/storing.

3. Performance

  • Two list comprehensions iterate over messages twice per chat record. Minor, but could be collapsed if both fields are needed. Not critical here.
  • Problem.objects.get_or_create(content=problem_text, ...) will now run with potentially different problem_text than before. If problem_text computation differs from what's stored in chat_record.problem_text, deduplication behavior changes. Ensure DB uniqueness constraints include knowledge_id.
  • Inside a loop, get_or_create and Paragraph construction mean N+1 writes; consider bulk_create for Paragraph / ProblemParagraphMapping batches and pre-fetching existing Problem rows with content__in=[...] to avoid repeated queries.

4. Empty data handling

  • If answer_text or problem_text ends up as "", you'll create empty Paragraph/Problem rows. Consider skipping records where answer_text.strip() == "" (or raising) to avoid polluting DB.

5. Style / type safety

  • The double iteration with a generator expression is fine, but typing could catch these issues earlier. Consider annotating chat_record.messages as List[Dict[str, str]] and letting mypy flag Optional[str] joins.
  • uuid.uuid7() — confirm your Python/dependency version actually provides uuid7 (not in stdlib until recently); otherwise this is a latent runtime error unrelated to this diff.

6. Recommend refactor

for chat_record in chat_record_list:
    messages = chat_record.messages or []
    answer_text = "\n".join(
        m["content"] for m in messages
        if m.get("type") == "TEXT" and isinstance(m.get("content"), str)
    )
    problem_text = ((chat_record.question or {}).get("content") or "").strip()
    if not answer_text or not problem_text:
        continue  # or log/skip
    ...

Summary: Main risks are None propagation into "".join, possible unintended semantics change (concatenation without separator), and DB-level empty rows / dedup regressions from recomputed problem_text. The core logic is sound; harden input handling and verify uniqueness assumptions.

Expand Down
61 changes: 20 additions & 41 deletions ui/src/workflow-canvas/ai-generate/application.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ import { WorkflowNodeType } from '@/workflow-canvas/types'
import { loopModeOf } from './tools/canvas'
import { inheritMessages, useAgentConfig } from './common'
import { createAgent } from './create-agent'
import { AgentSuccessResult, ErrorResult } from './tools/result'
import { ErrorResult } from './tools/result'
import { renderNodeFunction } from './tools/node'
import {
addEdgeTool,
Expand All @@ -22,7 +22,7 @@ import type { AgentBindings, AgentTool, RunnableAgent } from './type'

/**
* 应用工作流（Application）的父 agent：把每个节点当作一个函数（说明、形参、返回字段），
* 先委派规划器据需求组合出蓝图，再亲手建图（含循环体，经 lf_id 跨画布），字段配置委派给各节点配置 agent，最后校验。
* 先委派规划器据需求写出文字方案并经用户确认，再亲手建图（含循环体，经 lf_id 跨画布），字段配置委派给各节点配置 agent，最后校验。
*/

function nodeLine(type: string, label: string): string {
Expand Down Expand Up @@ -54,11 +54,12 @@ const GUIDE = `你是 MaxKB「智能体工作流」的搭建助手，在 LogicFl
- 画布用 lf_id 标识：主画布 "main"（或省略）；循环体 = 该循环节点 id。每个循环体固定有 loop-start-node（输出 index、item）。

【从零搭建】
1. 先调 plan：规划器据需求组合节点函数，产出蓝图 { blueprint, notes }。之后照蓝图建。
1. 先调 plan：规划器据需求组合节点函数，写出文字方案 { plan }（用户已在「规划流程」中看到全文）。拿到方案后先不要建图：不复述全文，用两三句概括主链路与关键假设，请用户确认或提出修改，然后直接结束本轮回复（不调用任何工具）。
用户确认后再照方案从第 2 步开始建；用户提出修改时，小改直接说明调整点后再次请用户确认，大改重新调 plan，同样确认后再建。
2. 用 add_node(type, source_anchor_id) 建节点并接上游：第一个节点接 "start-node"；普通链式连接传上游节点 id；判断器 / 意图识别要先 configure_node 配好分支（requirement 写清分几路、各路条件或分类），再 get_node_anchors 取各分支出口接下游；多分支汇合用 add_edge 补入边。
3. 循环：主画布 add_node 建 loop-node 并 configure_node 配循环方式；再带 lf_id=该循环节点 id 往循环体建（首节点 source_anchor_id="loop-start-node"），循环体内节点用 lf_id=该循环 id 配置；无限循环必须放 loop-break-node。
4. 节点建好接好后 configure_node(nodeId, args, requirement, returns?) 配置，相当于调用这个函数：
- args 传实参：形参名 → 上游引用 [节点ID, 字段]（多引用形参传列表），按蓝图的数据流给，框架会校验并绑定；
- args 传实参：形参名 → 上游引用 [节点ID, 字段]（多引用形参传列表），按方案中各节点的输入来源给，框架会校验并绑定；
- requirement 写配置要求（提示词只写要求、模型 / 知识库只写意图），节点配置 agent 会生成内容并查出真实资源；
- 节点有 [字段名] 动态返回时，returns 列出要求产出的字段名（如参数提取的 city、date），下游按这些字段引用。
同一轮可并发配置多个节点；但实参要引用上游的动态返回字段时，先配上游，按其交付的 outputs 再配下游。
Expand All @@ -73,49 +74,28 @@ function buildPrompt(bindings: AgentBindings): string {
return `${GUIDE}\n\n======== 可用节点 ========\n${renderCatalog(bindings)}`
}

const PLAN_GUIDE = `你是 MaxKB「智能体工作流」的规划器。下方【可用节点】把每个节点写成一个函数（说明、形参、返回字段）。根据用户需求，像写程序一样组合这些函数，产出一份执行蓝图供搭建 agent 照着建。你只规划、不改画布。
const PLAN_GUIDE = `你是 MaxKB「智能体工作流」的规划器。下方【可用节点】把每个节点写成一个函数（说明、形参、返回字段）。根据用户需求，像写程序一样组合这些函数，写出一份文字方案：先给用户确认，再供搭建 agent 照着建。你只规划、不改画布。

思路：
- 先拆解需求：最终要给用户什么输出；为得到它需要哪些中间数据；每份数据由哪个节点函数的返回值提供。
- 按数据依赖排顺序：每个节点的形参都要有上游返回值（或开始节点的 question、全局变量）来提供，类型要对得上（如循环的 array 需要上游返回 array）。
- 需要按条件 / 分类走不同处理时用分支（condition-node / intent-node），需要逐项处理一组数据时用循环（loop-node）。
- 只放需求实际需要的节点；末端必须是能输出给用户的节点，最终回答 is_result=true、中间步骤 false。
- 需要外部工具时先 get_tools 看有哪些可用工具。需求含糊时按最常见理解规划，并在 notes 写明假设。
- 需要外部工具时先 get_tools 看有哪些可用工具。需求含糊时按最常见理解规划，并写明假设。

blueprint 元素 { type, intent, args?, returns?, children?, branches? }：
- intent：一句话写清它在链路里做什么与配置要求（AI 对话的角色 / 目标 / 输出、检索什么、判断条件、表单收集哪些字段）；
- args：每个形参由谁提供，写成 形参名 → "上游节点.返回字段"（如 question → "开始.question"、materials → ["知识库检索.data", "开始.question"]）；
- returns：有 [字段名] 动态返回的节点，列出要产出的字段名与类型（如参数提取 { city: string, date: string }）；
- loop-node 用 children 装循环体；condition-node / intent-node 用 branches（[{cond, children}]，最后一个为 else / 其他）。
想清楚后调 submit_plan 交出，交出即结束。`
方案格式（Markdown，面向用户可读，同时足够搭建 agent 照着建）：
- 按执行顺序逐个列节点：节点名称（类型）——做什么与配置要求（AI 对话的角色 / 目标 / 输出、检索什么、判断条件、表单收集哪些字段）；输入：形参 ← 上游节点.返回字段（如 问题 ← 开始.question）；有 [字段名] 动态返回时写出要产出的字段与类型。
- 分支节点（判断器 / 意图识别）逐路写：条件或分类 → 该路的后续节点，最后一路为 else / 其他；循环节点写清循环方式与循环体内的节点。
- 最后写「关键假设」：需要用户事先准备或之后补充的内容（如知识库、工具），以及对含糊需求的理解。
写完方案直接结束，不调用任何工具。`

const PLAN_TASK = '结合用户这次的需求，组合可用节点函数设计出蓝图，调用 submit_plan 交出。'
const PLAN_TASK = '结合用户这次的需求，组合可用节点函数，按方案格式写出文字方案。'

function submitPlanTool(): AgentTool {
return {
name: 'submit_plan',
description: '交出规划：blueprint 节点树，notes 设计思路与关键假设。交出即结束。',
parameters: {
type: 'object',
properties: {
blueprint: { type: 'array', items: { type: 'object' } },
notes: { type: 'string' },
},
required: ['blueprint'],
},
apply: (args) =>
new AgentSuccessResult({
blueprint: Array.isArray(args.blueprint) ? args.blueprint : [],
notes: typeof args.notes === 'string' ? args.notes : '',
}),
}
}

/** 规划委派：起规划器子 agent（继承父对话），产出蓝图后上抛给父 agent */
/** 规划委派：起规划器子 agent（继承父对话），产出文字方案后上抛给父 agent */
function planTool(bindings: AgentBindings): AgentTool {
return {
name: 'plan',
description: '（从零搭建的第一步）委派规划器据需求组合节点函数并产出蓝图，返回 { blueprint, notes }。',
description: '（从零搭建的第一步，或用户要求大改方案时）委派规划器据需求组合节点函数，写出文字方案，返回 { plan }。',
parameters: { type: 'object', properties: {} },
apply: () => {
const plannerBindings: AgentBindings = {
Expand All @@ -128,17 +108,16 @@ function planTool(bindings: AgentBindings): AgentTool {
useAgentConfig(
{
prompt: `${PLAN_GUIDE}\n\n======== 可用节点 ========\n${renderCatalog(bindings)}`,
tools: [getToolsTool(bindings), submitPlanTool()],
tools: [getToolsTool(bindings)],
},
plannerBindings,
),
)
// 规划器不调用工具即结束，data 为最后一段正文
const promise = planner.run(PLAN_TASK).then((result) => {
const plan = result.data as { blueprint?: unknown[] } | undefined
if (result.status !== 'done' || !plan || typeof plan !== 'object' || !plan.blueprint?.length) {
return new ErrorResult(typeof result.data === 'string' && result.data ? result.data : '规划未产出蓝图')
}
return plan
const plan = typeof result.data === 'string' ? result.data.trim() : ''
if (result.status !== 'done' || !plan) return new ErrorResult(plan || '规划未产出方案')
return { plan }
})
return { promise, cancel: planner.stop }
},
Expand Down
12 changes: 10 additions & 2 deletions ui/src/workflow-canvas/ai-generate/panel/AiGeneratePanel.vue
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
<script setup lang="ts">
import { computed, inject, nextTick, onBeforeUnmount, ref, useTemplateRef } from 'vue'
import { computed, inject, nextTick, onBeforeUnmount, ref, useTemplateRef ,provide} from 'vue'
import { useRoute } from 'vue-router'
import { cloneDeep } from 'lodash'
import type LogicFlow from '@logicflow/core'
Expand All @@ -18,7 +18,8 @@ import { clearDebugLog } from '../common'
import { createAgent } from '../create-agent'
import type { AgentEvent, AgentHandle } from '../type'
import { createActivity } from './activity'

import { VIEW_KEY,VIEW_TYPE } from '@/conversation-panel/view'
provide(VIEW_KEY,VIEW_TYPE.DEBUG)
defineOptions({ name: 'WorkflowAiGeneratePanel' })
const props = withDefaults(
defineProps<{
Expand Down Expand Up @@ -90,8 +91,13 @@ function scrollToBottom() {
nextTick(() => scrollbarRef.value?.setScrollTop(Number.MAX_SAFE_INTEGER))
}

/** 会改动画布的顶层工具；本轮未调用时视为仅规划 / 答复（如等待用户确认方案），不提示生成完成 */
const CANVAS_MUTATING_TOOLS = new Set(['add_node', 'add_edge', 'delete_node', 'delete_edge', 'configure_node', 'layout'])
let canvasChanged = false

/** 顶层 agent 的总结已作为正文流入活动，失败作为 FAILURE 项展示 */
function onEvent(event: AgentEvent) {
if (event.type === 'tool_start' && !event.position.length && CANVAS_MUTATING_TOOLS.has(event.name)) canvasChanged = true
activity.pushEvent(event)
scrollToBottom()
}
Expand All @@ -117,9 +123,11 @@ async function run(text: string) {
)
}
status.value = 'running'
canvasChanged = false
const result = await agent.run(text)
if (result.status === 'done') {
status.value = 'done'
if (!canvasChanged) return
await layoutCanvas(lf)
lf.fitView()
MsgSuccess('工作流已生成，请检查后保存')

@shaohuzhang1 shaohuzhang1 Nov 9, 2026

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Summary of Issues and Suggestions:

  1. Syntax/Formatting Error in Diff (Critical)

    • Line 2: ref, useTemplateRef ,provide} has an extra space before the comma: useTemplateRef ,provide. While valid JS, it violates standard formatting rules (e.g., ESLint/Prettier).
    • Line 23: import { VIEW_KEY,VIEW_TYPE } from ... lacks a space after the comment and before the import statement (import type AgentEvent... on line 22 vs line 23). It also misses a blank line separator often expected by linters.

  2. Unused Variable / Dead Code Risk

    • provide(VIEW_KEY, VIEW_TYPE.DEBUG) is called at the top level of <script setup>. Ensure that this provide is necessary for this component. If WorkflowAiGeneratePanel is not a parent of the components relying on VIEW_KEY, this will have no effect or be misleading. If it is intended, ensure VIEW_KEY and VIEW_TYPE are correctly exported from @/conversation-panel/view.

  3. Logic Flaw in onEvent Condition

    • !event.position.length: If position is not an array (e.g., undefined or null), this will throw TypeError: Cannot read properties of undefined (reading 'length').
    • Fix: Use optional chaining: !event.position?.length or check truthiness if empty array should trigger it differently. However, based on context, you likely want to detect if position is empty/missing indicating it's not a sub-tool event. If position is always supposed to be an array, initialize it. If it can be undefined, use !event.position || !event.position.length.

  4. State Management Issue (canvasChanged)

    • let canvasChanged = false; inside <script setup> works because module-level variables in <script setup> are isolated per component instance. However, if run() is called multiple times concurrently (race condition), they share the same canvasChanged flag. This could lead to incorrect state if two runs overlap.
    • Suggestion: Make canvasChanged local to the run function scope by passing it via closure or resetting it properly and ensuring sequential execution, or move it into activeIdsMap or similar state tracking if concurrent runs are possible.

  5. Optimization/Readability: Set Declaration

    • const CANVAS_MUTATING_TOOLS = new Set([...]) is declared inside the script but outside any function. This is fine and efficient (created once per component setup). No issue, just good practice confirmed.

  6. Missing Error Handling Edge Case

    • In run(), if result.status !== 'done' (e.g., error/cancelled), status.value remains 'running' until manually reset elsewhere? Check how errors are handled. The current code only updates status on 'done'. Ensure failure states reset status.value appropriately in other branches not shown.

  7. Import Sorting/Linter

    • provide is added but placed incorrectly relative to alphabet/standard order (ref, useTemplateRef ,provide → should be provide, ref, useTemplateRef). Some linters enforce alphabetical order.

✅ Recommended Fixed Code Snippet (Diff Style)

     @@ -1,5 +1,5 @@
 <script setup lang="ts">
-import { computed, inject, nextTick, onBeforeUnmount, ref, useTemplateRef } from 'vue'
+import { computed, inject, provide, nextTick, onBeforeUnmount, ref, useTemplateRef } from 'vue' // Alphabetical order
 import { useRoute } from 'vue-router'
 import { cloneDeep } from 'lodash'
 import type LogicFlow from '@logicflow/core'
@@ -18,8 +18,9 @@ import { clearDebugLog } from '../common'
 import { createAgent } from '../create-agent'
 import type { AgentEvent, AgentHandle } from '../type'
 import { createActivity } from './activity'
-
+// Add blank line if required by linter, fix spacing
+import { VIEW_KEY, VIEW_TYPE } from '@/conversation-panel/view'
 // Provide view key for debugging context (ensure parent-child relationship exists)
 provide(VIEW_KEY, VIEW_TYPE.DEBUG)
 
 defineOptions({ name: 'WorkflowAiGeneratePanel' })
 const props = withDefaults(
   defineProps<{
@@ -90,9 +91,9 @@ function scrollToBottom() {
   nextTick(() => scrollbarRef.value?.setScrollTop(Number.MAX_SAFE_INTEGER))
 }
 
-/** 会改动画布的顶层工具；本轮未调用时视为仅规划 / 答复（如等待用户确认方案），不提示生成完成 */
+/** Tools that mutate canvas. If none are called, skip layout/fitView (e.g., planning-only mode) */
 const CANVAS_MUTATING_TOOLS = new Set(['add_node', 'add_edge', 'delete_node', 'delete_edge', 'configure_node', 'layout'])
 let canvasChanged = false
 
 /** Top-level agent summary flows into activity as text; failures shown as FAILURE items */
 function onEvent(event: AgentEvent) {
-  if (event.type === 'tool_start' && !event.position.length && CANVAS_MUTATING_TOOLS.has(event.name)) canvasChanged = true
+  // Safe check: handle if position is undefined/null
+  const isTopLevel = !event.position || event.position.length === 0
+  if (event.type === 'tool_start' && isTopLevel && CANVAS_MUTATING_TOOLS.has(event.name)) {
+    canvasChanged = true
+  }
   activity.pushEvent(event)
   scrollToBottom()
 }
@@ -117,12 +123,14 @@ async function run(text: string) {
     )
   }
   status.value = 'running'
   canvasChanged = false
   const result = await agent.run(text)
   if (result.status === 'done') {
     status.value = 'done'
     // Only fit view and show success if canvas actually changed
-    if (!canvasChanged) return
+    if (!canvasChanged) {
+      // Optional: Show a different message or do nothing silently if only planning occurred
+      return 
+    }
     await layoutCanvas(lf)
     lf.fitView()
     MsgSuccess('工作流已生成，请检查后保存')

Expand Down
1 change: 0 additions & 1 deletion ui/src/workflow-canvas/ai-generate/panel/activity.ts
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,6 @@ const TOOL_LABELS: Record<string, string> = {
configure_node: '配置节点',
validate: '校验工作流',
layout: '整理布局',
submit_plan: '提交规划',
get_field_options: '查看可引用变量',
get_models: '查询模型',
get_knowledge_list: '查询知识库',
Expand Down
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